Missing Interest Modeling with Lifelong User Behavior Data for Retrieval Recommendation

Gaode Chen, Yuezihan Jiang, Rui Huang, Kuo Cai, Yunze Luo, Ruina Sun, Qi Zhang, Han Li, Kun Gai

Abstract: Rich user behavior data has been proven to be of great value for recommendation systems. Modeling lifelong user behavior data in the retrieval stage to explore user long-term preference and obtain comprehensive retrieval results is crucial. Existing lifelong modeling methods cannot applied to the retrieval stage because they extract target-relevant items through the coupling between the user and the target item. Moreover, the current retrieval methods fail to precisely capture user interests when the length of the user behavior sequence increases further. That leads to a gap in the ability of retrieval models to model lifelong user behavior data. In this paper, we propose the concept of missing interest, leveraging the idea of complementarity, which serves as a supplement to short-term interest based on lifelong behavior data in the retrieval stage. Specifically, we design a missing interest operator and deploy it in Kafka data stream, without incurring latency or storage costs. This operator derives categories and authors of items that the user was previously interested in but has recently missed, and uses these as triggers to output missing features to the downstream retrieval model. Our retrieval model is a complete dual-tower structure that combines short-term and missing interests on the user side to provide a comprehensive depiction of lifelong behaviors. Since 2023, the presented solution has been deployed in Kuaishou, one of the most popular short-video streaming platforms in China with hundreds of millions of active users.